Integrating Predictive Maintenance in Adaptive Process Scheduling for a Safe and Efficient Industrial Process

Abstract

Predictive maintenance (PM) algorithms are widely applied for detecting operational anomalies on industrial processes to schedule for a maintenance intervention before a possible breakdown; however, much less focus has been devoted to the use of such prognostics in process scheduling. The existing solutions mostly integrate preventive approaches to protect the machines, usually causing downtimes. The premise of this study is to develop a process scheduling mechanism that selects an acceptable operating condition for an industrial process to adapt to the predicted anomalies. As PM is largely a data-driven approach (hence, it relies on the setup), we first compare different PM approaches and identify a one-class support vector machine (OCSVM) as the best performing option for the anomaly detection on our setup. Then, we propose a novel pipeline to integrate maintenance predictions into a real-time, adaptive process scheduling mechanism. According to the abnormal readings, it schedules for the most suitable operation, i.e., optimizing for machine health and process efficiency, toward preventing breakdowns while maintaining its availability and operational state, thereby reducing downtimes. To demonstrate the pipeline on the action, we implement our approach on a small-scale conveyor belt, utilizing our Internet of Things (IoT) framework. The results show that our PM-based adaptive process control retains an efficient process under abnormal conditions with less or no downtime. We also conclude that a PM approach does not provide sufficient efficiency without its integration into an autonomous planning process.

Learning-Based Algorithm for Fault Prediction Combining Different Data Mining Techniques: A Real Case Study

“…In the current scenario, data-driven approaches are used by companies to support decision-makers in managing the wide amount of data in multiple contexts: energy consumption (Mugnini et al, 2021), industrial operations productivity (Antomarioni et al, 2021), efficiency (Görür et al, 2021), sustainability (Linke et al, 2019) and so on. Due to the integration of data analytics techniques and the advancement of information technologies, both efficiency and productivity areas have demonstrated the most potential improvement in manufacturing (Mansouri et al, 2020), mainly for failure detection in the field of maintenance (Görür et al, 2021).…”